DocumentCode
3241634
Title
Convergence Properties of Particle Filter Algorithm
Author
Qu, Yanwen ; Chen, Yi ; Yang, Jingyu
Author_Institution
Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing
fYear
2008
fDate
22-24 Oct. 2008
Firstpage
1
Lastpage
6
Abstract
The basic sampling importance resampling algorithm is the basic for improving particle filter methods which are widely utilized in optimal filtering problems. In our paper, we introduce a modified basic SIR algorithm and analyze the convergence property of the modified basic SIR algorithm. Furthermore, when the recursive time is finite and the forth-order moment of the interesting function w.r.t the posterior joint distribution of the extended state is exist, the sufficient condition for the basic particle filter estimation convergence almost surely to the optimal estimation is discussed.
Keywords
importance sampling; particle filtering (numerical methods); convergence property; particle filter algorithm; posterior joint distribution; sampling importance resampling algorithm; Computer science; Convergence; Electronic mail; Filtering algorithms; Monte Carlo methods; Particle filters; Recursive estimation; Sampling methods; State estimation; Yttrium;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2008. CCPR '08. Chinese Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-2316-3
Type
conf
DOI
10.1109/CCPR.2008.14
Filename
4662967
Link To Document